Printed circuit boards (PCBs) are critical for interconnecting various components and allowing them to communicate with each other. It is critical to ensure that there are no small surface defects that can negatively impact PCB production. Therefore, template matching is often used in PCB surface inspection systems. Despite its popularity, this method can be improved because inspecting a PCB with a template is inefficient. Currently, integrating the surface inspection system with the deep learning method is proving to be more effective in solving this problem. This paper examines three popular deep learning object recognition methods in order to determine which one is the most effective in terms of attention. These three models are called Carafe, Empirical Attention, and ResNeSt. The experimental results showed that ResNeSt with split attention networks achieves the greatest accuracy in deep learning PCB surface inspection system with a mean average precision (mAP) of 99.2% and an average recall (AR) of 99.5%. The result of this study would improve the effectiveness of PCB surface inspection in controlling production lines.